Ford GoBike System Data¶

by Hatem Hassan ElDaly¶

Introduction¶

This data set includes information about individual rides made in a bike-sharing system covering the greater San Francisco Bay area.

Preliminary Wrangling¶

In [1]:
# Import all packages that we going to use
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from scipy import stats
import plotly.express as px

%matplotlib inline
In [2]:
# Load the dataset and print the first rows to check the dataset
df = pd.read_csv('201902-fordgobike-tripdata.csv')
df.head()
Out[2]:
duration_sec start_time end_time start_station_id start_station_name start_station_latitude start_station_longitude end_station_id end_station_name end_station_latitude end_station_longitude bike_id user_type member_birth_year member_gender bike_share_for_all_trip
0 52185 2019-02-28 17:32:10.1450 2019-03-01 08:01:55.9750 21.0 Montgomery St BART Station (Market St at 2nd St) 37.789625 -122.400811 13.0 Commercial St at Montgomery St 37.794231 -122.402923 4902 Customer 1984.0 Male No
1 42521 2019-02-28 18:53:21.7890 2019-03-01 06:42:03.0560 23.0 The Embarcadero at Steuart St 37.791464 -122.391034 81.0 Berry St at 4th St 37.775880 -122.393170 2535 Customer NaN NaN No
2 61854 2019-02-28 12:13:13.2180 2019-03-01 05:24:08.1460 86.0 Market St at Dolores St 37.769305 -122.426826 3.0 Powell St BART Station (Market St at 4th St) 37.786375 -122.404904 5905 Customer 1972.0 Male No
3 36490 2019-02-28 17:54:26.0100 2019-03-01 04:02:36.8420 375.0 Grove St at Masonic Ave 37.774836 -122.446546 70.0 Central Ave at Fell St 37.773311 -122.444293 6638 Subscriber 1989.0 Other No
4 1585 2019-02-28 23:54:18.5490 2019-03-01 00:20:44.0740 7.0 Frank H Ogawa Plaza 37.804562 -122.271738 222.0 10th Ave at E 15th St 37.792714 -122.248780 4898 Subscriber 1974.0 Male Yes
In [3]:
# Get an overview of the dataset and datatypes of rach column and non-null values
df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 183412 entries, 0 to 183411
Data columns (total 16 columns):
 #   Column                   Non-Null Count   Dtype  
---  ------                   --------------   -----  
 0   duration_sec             183412 non-null  int64  
 1   start_time               183412 non-null  object 
 2   end_time                 183412 non-null  object 
 3   start_station_id         183215 non-null  float64
 4   start_station_name       183215 non-null  object 
 5   start_station_latitude   183412 non-null  float64
 6   start_station_longitude  183412 non-null  float64
 7   end_station_id           183215 non-null  float64
 8   end_station_name         183215 non-null  object 
 9   end_station_latitude     183412 non-null  float64
 10  end_station_longitude    183412 non-null  float64
 11  bike_id                  183412 non-null  int64  
 12  user_type                183412 non-null  object 
 13  member_birth_year        175147 non-null  float64
 14  member_gender            175147 non-null  object 
 15  bike_share_for_all_trip  183412 non-null  object 
dtypes: float64(7), int64(2), object(7)
memory usage: 22.4+ MB
In [4]:
# Get statistical overview of all the dataset
df.describe(include = 'all')
Out[4]:
duration_sec start_time end_time start_station_id start_station_name start_station_latitude start_station_longitude end_station_id end_station_name end_station_latitude end_station_longitude bike_id user_type member_birth_year member_gender bike_share_for_all_trip
count 183412.000000 183412 183412 183215.000000 183215 183412.000000 183412.000000 183215.000000 183215 183412.000000 183412.000000 183412.000000 183412 175147.000000 175147 183412
unique NaN 183401 183397 NaN 329 NaN NaN NaN 329 NaN NaN NaN 2 NaN 3 2
top NaN 2019-02-25 08:52:07.5820 2019-02-11 08:03:22.6020 NaN Market St at 10th St NaN NaN NaN San Francisco Caltrain Station 2 (Townsend St... NaN NaN NaN Subscriber NaN Male No
freq NaN 2 2 NaN 3904 NaN NaN NaN 4857 NaN NaN NaN 163544 NaN 130651 166053
mean 726.078435 NaN NaN 138.590427 NaN 37.771223 -122.352664 136.249123 NaN 37.771427 -122.352250 4472.906375 NaN 1984.806437 NaN NaN
std 1794.389780 NaN NaN 111.778864 NaN 0.099581 0.117097 111.515131 NaN 0.099490 0.116673 1664.383394 NaN 10.116689 NaN NaN
min 61.000000 NaN NaN 3.000000 NaN 37.317298 -122.453704 3.000000 NaN 37.317298 -122.453704 11.000000 NaN 1878.000000 NaN NaN
25% 325.000000 NaN NaN 47.000000 NaN 37.770083 -122.412408 44.000000 NaN 37.770407 -122.411726 3777.000000 NaN 1980.000000 NaN NaN
50% 514.000000 NaN NaN 104.000000 NaN 37.780760 -122.398285 100.000000 NaN 37.781010 -122.398279 4958.000000 NaN 1987.000000 NaN NaN
75% 796.000000 NaN NaN 239.000000 NaN 37.797280 -122.286533 235.000000 NaN 37.797320 -122.288045 5502.000000 NaN 1992.000000 NaN NaN
max 85444.000000 NaN NaN 398.000000 NaN 37.880222 -121.874119 398.000000 NaN 37.880222 -121.874119 6645.000000 NaN 2001.000000 NaN NaN
In [5]:
# Checking the values of user_type column
df.user_type.value_counts()
Out[5]:
Subscriber    163544
Customer       19868
Name: user_type, dtype: int64
In [6]:
# # Checking the values of member_gender column
df.member_gender.value_counts()
Out[6]:
Male      130651
Female     40844
Other       3652
Name: member_gender, dtype: int64
In [7]:
# Drop all NaN values from the table
df = df.dropna()
df.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 174952 entries, 0 to 183411
Data columns (total 16 columns):
 #   Column                   Non-Null Count   Dtype  
---  ------                   --------------   -----  
 0   duration_sec             174952 non-null  int64  
 1   start_time               174952 non-null  object 
 2   end_time                 174952 non-null  object 
 3   start_station_id         174952 non-null  float64
 4   start_station_name       174952 non-null  object 
 5   start_station_latitude   174952 non-null  float64
 6   start_station_longitude  174952 non-null  float64
 7   end_station_id           174952 non-null  float64
 8   end_station_name         174952 non-null  object 
 9   end_station_latitude     174952 non-null  float64
 10  end_station_longitude    174952 non-null  float64
 11  bike_id                  174952 non-null  int64  
 12  user_type                174952 non-null  object 
 13  member_birth_year        174952 non-null  float64
 14  member_gender            174952 non-null  object 
 15  bike_share_for_all_trip  174952 non-null  object 
dtypes: float64(7), int64(2), object(7)
memory usage: 22.7+ MB
In [8]:
# Change the data type of 'user_type' and 'member_gender' columns into category data type 
df.user_type = df.user_type.astype('category')
df.member_gender = df.member_gender.astype('category')
df.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 174952 entries, 0 to 183411
Data columns (total 16 columns):
 #   Column                   Non-Null Count   Dtype   
---  ------                   --------------   -----   
 0   duration_sec             174952 non-null  int64   
 1   start_time               174952 non-null  object  
 2   end_time                 174952 non-null  object  
 3   start_station_id         174952 non-null  float64 
 4   start_station_name       174952 non-null  object  
 5   start_station_latitude   174952 non-null  float64 
 6   start_station_longitude  174952 non-null  float64 
 7   end_station_id           174952 non-null  float64 
 8   end_station_name         174952 non-null  object  
 9   end_station_latitude     174952 non-null  float64 
 10  end_station_longitude    174952 non-null  float64 
 11  bike_id                  174952 non-null  int64   
 12  user_type                174952 non-null  category
 13  member_birth_year        174952 non-null  float64 
 14  member_gender            174952 non-null  category
 15  bike_share_for_all_trip  174952 non-null  object  
dtypes: category(2), float64(7), int64(2), object(5)
memory usage: 20.4+ MB
In [9]:
# Change the data type of 'start_time' and 'end_time' columns into datetime data type 
df.start_time = pd.to_datetime(df.start_time)
df.end_time = pd.to_datetime(df.end_time)
df.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 174952 entries, 0 to 183411
Data columns (total 16 columns):
 #   Column                   Non-Null Count   Dtype         
---  ------                   --------------   -----         
 0   duration_sec             174952 non-null  int64         
 1   start_time               174952 non-null  datetime64[ns]
 2   end_time                 174952 non-null  datetime64[ns]
 3   start_station_id         174952 non-null  float64       
 4   start_station_name       174952 non-null  object        
 5   start_station_latitude   174952 non-null  float64       
 6   start_station_longitude  174952 non-null  float64       
 7   end_station_id           174952 non-null  float64       
 8   end_station_name         174952 non-null  object        
 9   end_station_latitude     174952 non-null  float64       
 10  end_station_longitude    174952 non-null  float64       
 11  bike_id                  174952 non-null  int64         
 12  user_type                174952 non-null  category      
 13  member_birth_year        174952 non-null  float64       
 14  member_gender            174952 non-null  category      
 15  bike_share_for_all_trip  174952 non-null  object        
dtypes: category(2), datetime64[ns](2), float64(7), int64(2), object(3)
memory usage: 20.4+ MB
In [10]:
# change the data type of 'bike_share_for_all_trip' into boolean data type
df.bike_share_for_all_trip = (df.bike_share_for_all_trip == 'Yes')
df.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 174952 entries, 0 to 183411
Data columns (total 16 columns):
 #   Column                   Non-Null Count   Dtype         
---  ------                   --------------   -----         
 0   duration_sec             174952 non-null  int64         
 1   start_time               174952 non-null  datetime64[ns]
 2   end_time                 174952 non-null  datetime64[ns]
 3   start_station_id         174952 non-null  float64       
 4   start_station_name       174952 non-null  object        
 5   start_station_latitude   174952 non-null  float64       
 6   start_station_longitude  174952 non-null  float64       
 7   end_station_id           174952 non-null  float64       
 8   end_station_name         174952 non-null  object        
 9   end_station_latitude     174952 non-null  float64       
 10  end_station_longitude    174952 non-null  float64       
 11  bike_id                  174952 non-null  int64         
 12  user_type                174952 non-null  category      
 13  member_birth_year        174952 non-null  float64       
 14  member_gender            174952 non-null  category      
 15  bike_share_for_all_trip  174952 non-null  bool          
dtypes: bool(1), category(2), datetime64[ns](2), float64(7), int64(2), object(2)
memory usage: 19.2+ MB
In [11]:
# Change the data type of ['start_station_latitude', 'start_station_longitude', 'end_station_latitude', 'end_station_longitude', 'bike_id'] from float into string
str_columns = ['start_station_latitude', 'start_station_longitude', 'end_station_latitude', 'end_station_longitude', 'bike_id']
for x in str_columns:
    df[x] = df[x].astype(str)
df.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 174952 entries, 0 to 183411
Data columns (total 16 columns):
 #   Column                   Non-Null Count   Dtype         
---  ------                   --------------   -----         
 0   duration_sec             174952 non-null  int64         
 1   start_time               174952 non-null  datetime64[ns]
 2   end_time                 174952 non-null  datetime64[ns]
 3   start_station_id         174952 non-null  float64       
 4   start_station_name       174952 non-null  object        
 5   start_station_latitude   174952 non-null  object        
 6   start_station_longitude  174952 non-null  object        
 7   end_station_id           174952 non-null  float64       
 8   end_station_name         174952 non-null  object        
 9   end_station_latitude     174952 non-null  object        
 10  end_station_longitude    174952 non-null  object        
 11  bike_id                  174952 non-null  object        
 12  user_type                174952 non-null  category      
 13  member_birth_year        174952 non-null  float64       
 14  member_gender            174952 non-null  category      
 15  bike_share_for_all_trip  174952 non-null  bool          
dtypes: bool(1), category(2), datetime64[ns](2), float64(3), int64(1), object(7)
memory usage: 19.2+ MB
In [12]:
# Make new columns for start time in 24H's, the start day of the week, and start date only, and members age 
df['start_hour_of_the_day'] = df.start_time.dt.strftime('%H')
df['start_day_of_the_week'] = df.start_time.dt.strftime('%A')
df['start_date'] = df.start_time.dt.strftime('%Y-%m-%d')
df['member_age'] = 2022 - df.member_birth_year
df.head()
Out[12]:
duration_sec start_time end_time start_station_id start_station_name start_station_latitude start_station_longitude end_station_id end_station_name end_station_latitude end_station_longitude bike_id user_type member_birth_year member_gender bike_share_for_all_trip start_hour_of_the_day start_day_of_the_week start_date member_age
0 52185 2019-02-28 17:32:10.145 2019-03-01 08:01:55.975 21.0 Montgomery St BART Station (Market St at 2nd St) 37.7896254 -122.400811 13.0 Commercial St at Montgomery St 37.794231 -122.402923 4902 Customer 1984.0 Male False 17 Thursday 2019-02-28 38.0
2 61854 2019-02-28 12:13:13.218 2019-03-01 05:24:08.146 86.0 Market St at Dolores St 37.7693053 -122.4268256 3.0 Powell St BART Station (Market St at 4th St) 37.78637526861584 -122.40490436553954 5905 Customer 1972.0 Male False 12 Thursday 2019-02-28 50.0
3 36490 2019-02-28 17:54:26.010 2019-03-01 04:02:36.842 375.0 Grove St at Masonic Ave 37.77483629413345 -122.44654566049576 70.0 Central Ave at Fell St 37.77331087889723 -122.44429260492323 6638 Subscriber 1989.0 Other False 17 Thursday 2019-02-28 33.0
4 1585 2019-02-28 23:54:18.549 2019-03-01 00:20:44.074 7.0 Frank H Ogawa Plaza 37.8045623549303 -122.27173805236816 222.0 10th Ave at E 15th St 37.7927143 -122.2487796 4898 Subscriber 1974.0 Male True 23 Thursday 2019-02-28 48.0
5 1793 2019-02-28 23:49:58.632 2019-03-01 00:19:51.760 93.0 4th St at Mission Bay Blvd S 37.7704074 -122.3911984 323.0 Broadway at Kearny 37.79801364395978 -122.40595042705534 5200 Subscriber 1959.0 Male False 23 Thursday 2019-02-28 63.0
In [13]:
df.member_birth_year = df.member_birth_year.astype('int')
df.member_age = df.member_age.astype('int')
df.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 174952 entries, 0 to 183411
Data columns (total 20 columns):
 #   Column                   Non-Null Count   Dtype         
---  ------                   --------------   -----         
 0   duration_sec             174952 non-null  int64         
 1   start_time               174952 non-null  datetime64[ns]
 2   end_time                 174952 non-null  datetime64[ns]
 3   start_station_id         174952 non-null  float64       
 4   start_station_name       174952 non-null  object        
 5   start_station_latitude   174952 non-null  object        
 6   start_station_longitude  174952 non-null  object        
 7   end_station_id           174952 non-null  float64       
 8   end_station_name         174952 non-null  object        
 9   end_station_latitude     174952 non-null  object        
 10  end_station_longitude    174952 non-null  object        
 11  bike_id                  174952 non-null  object        
 12  user_type                174952 non-null  category      
 13  member_birth_year        174952 non-null  int32         
 14  member_gender            174952 non-null  category      
 15  bike_share_for_all_trip  174952 non-null  bool          
 16  start_hour_of_the_day    174952 non-null  object        
 17  start_day_of_the_week    174952 non-null  object        
 18  start_date               174952 non-null  object        
 19  member_age               174952 non-null  int32         
dtypes: bool(1), category(2), datetime64[ns](2), float64(2), int32(2), int64(1), object(10)
memory usage: 23.2+ MB
In [14]:
# Make a new column with duration og the trip in mins instead of seconds
df['duration_min'] = df.duration_sec / 60
df.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 174952 entries, 0 to 183411
Data columns (total 21 columns):
 #   Column                   Non-Null Count   Dtype         
---  ------                   --------------   -----         
 0   duration_sec             174952 non-null  int64         
 1   start_time               174952 non-null  datetime64[ns]
 2   end_time                 174952 non-null  datetime64[ns]
 3   start_station_id         174952 non-null  float64       
 4   start_station_name       174952 non-null  object        
 5   start_station_latitude   174952 non-null  object        
 6   start_station_longitude  174952 non-null  object        
 7   end_station_id           174952 non-null  float64       
 8   end_station_name         174952 non-null  object        
 9   end_station_latitude     174952 non-null  object        
 10  end_station_longitude    174952 non-null  object        
 11  bike_id                  174952 non-null  object        
 12  user_type                174952 non-null  category      
 13  member_birth_year        174952 non-null  int32         
 14  member_gender            174952 non-null  category      
 15  bike_share_for_all_trip  174952 non-null  bool          
 16  start_hour_of_the_day    174952 non-null  object        
 17  start_day_of_the_week    174952 non-null  object        
 18  start_date               174952 non-null  object        
 19  member_age               174952 non-null  int32         
 20  duration_min             174952 non-null  float64       
dtypes: bool(1), category(2), datetime64[ns](2), float64(3), int32(2), int64(1), object(10)
memory usage: 24.5+ MB
In [15]:
# Get an overview statistics of the new table
df.describe(include = 'all')

C:\Users\aswan\AppData\Local\Temp\ipykernel_12832\2418990127.py:2: FutureWarning: Treating datetime data as categorical rather than numeric in `.describe` is deprecated and will be removed in a future version of pandas. Specify `datetime_is_numeric=True` to silence this warning and adopt the future behavior now.
  df.describe(include = 'all')
C:\Users\aswan\AppData\Local\Temp\ipykernel_12832\2418990127.py:2: FutureWarning: Treating datetime data as categorical rather than numeric in `.describe` is deprecated and will be removed in a future version of pandas. Specify `datetime_is_numeric=True` to silence this warning and adopt the future behavior now.
  df.describe(include = 'all')
Out[15]:
duration_sec start_time end_time start_station_id start_station_name start_station_latitude start_station_longitude end_station_id end_station_name end_station_latitude ... bike_id user_type member_birth_year member_gender bike_share_for_all_trip start_hour_of_the_day start_day_of_the_week start_date member_age duration_min
count 174952.000000 174952 174952 174952.000000 174952 174952 174952 174952.000000 174952 174952 ... 174952 174952 174952.000000 174952 174952 174952 174952 174952 174952.000000 174952.000000
unique NaN 174941 174939 NaN 329 329 329 NaN 329 329 ... 4607 2 NaN 3 2 24 7 28 NaN NaN
top NaN 2019-02-11 17:05:07.840000 2019-02-28 17:40:37.328000 NaN Market St at 10th St 37.776619 -122.417385 NaN San Francisco Caltrain Station 2 (Townsend St... 37.7766392 ... 4794 Subscriber NaN Male False 17 Thursday 2019-02-28 NaN NaN
freq NaN 2 2 NaN 3649 3649 3649 NaN 4624 4624 ... 179 158386 NaN 130500 157606 20904 33712 9448 NaN NaN
first NaN 2019-02-01 00:00:20.636000 2019-02-01 00:04:52.058000 NaN NaN NaN NaN NaN NaN NaN ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
last NaN 2019-02-28 23:59:18.548000 2019-03-01 08:01:55.975000 NaN NaN NaN NaN NaN NaN NaN ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
mean 704.002744 NaN NaN 139.002126 NaN NaN NaN 136.604486 NaN NaN ... NaN NaN 1984.803135 NaN NaN NaN NaN NaN 37.196865 11.733379
std 1642.204905 NaN NaN 111.648819 NaN NaN NaN 111.335635 NaN NaN ... NaN NaN 10.118731 NaN NaN NaN NaN NaN 10.118731 27.370082
min 61.000000 NaN NaN 3.000000 NaN NaN NaN 3.000000 NaN NaN ... NaN NaN 1878.000000 NaN NaN NaN NaN NaN 21.000000 1.016667
25% 323.000000 NaN NaN 47.000000 NaN NaN NaN 44.000000 NaN NaN ... NaN NaN 1980.000000 NaN NaN NaN NaN NaN 30.000000 5.383333
50% 510.000000 NaN NaN 104.000000 NaN NaN NaN 101.000000 NaN NaN ... NaN NaN 1987.000000 NaN NaN NaN NaN NaN 35.000000 8.500000
75% 789.000000 NaN NaN 239.000000 NaN NaN NaN 238.000000 NaN NaN ... NaN NaN 1992.000000 NaN NaN NaN NaN NaN 42.000000 13.150000
max 84548.000000 NaN NaN 398.000000 NaN NaN NaN 398.000000 NaN NaN ... NaN NaN 2001.000000 NaN NaN NaN NaN NaN 144.000000 1409.133333

13 rows × 21 columns

In [16]:
# Get the first 10 rows of the data for better understanding of the data
df.head(10)
Out[16]:
duration_sec start_time end_time start_station_id start_station_name start_station_latitude start_station_longitude end_station_id end_station_name end_station_latitude ... bike_id user_type member_birth_year member_gender bike_share_for_all_trip start_hour_of_the_day start_day_of_the_week start_date member_age duration_min
0 52185 2019-02-28 17:32:10.145 2019-03-01 08:01:55.975 21.0 Montgomery St BART Station (Market St at 2nd St) 37.7896254 -122.400811 13.0 Commercial St at Montgomery St 37.794231 ... 4902 Customer 1984 Male False 17 Thursday 2019-02-28 38 869.750000
2 61854 2019-02-28 12:13:13.218 2019-03-01 05:24:08.146 86.0 Market St at Dolores St 37.7693053 -122.4268256 3.0 Powell St BART Station (Market St at 4th St) 37.78637526861584 ... 5905 Customer 1972 Male False 12 Thursday 2019-02-28 50 1030.900000
3 36490 2019-02-28 17:54:26.010 2019-03-01 04:02:36.842 375.0 Grove St at Masonic Ave 37.77483629413345 -122.44654566049576 70.0 Central Ave at Fell St 37.77331087889723 ... 6638 Subscriber 1989 Other False 17 Thursday 2019-02-28 33 608.166667
4 1585 2019-02-28 23:54:18.549 2019-03-01 00:20:44.074 7.0 Frank H Ogawa Plaza 37.8045623549303 -122.27173805236816 222.0 10th Ave at E 15th St 37.7927143 ... 4898 Subscriber 1974 Male True 23 Thursday 2019-02-28 48 26.416667
5 1793 2019-02-28 23:49:58.632 2019-03-01 00:19:51.760 93.0 4th St at Mission Bay Blvd S 37.7704074 -122.3911984 323.0 Broadway at Kearny 37.79801364395978 ... 5200 Subscriber 1959 Male False 23 Thursday 2019-02-28 63 29.883333
6 1147 2019-02-28 23:55:35.104 2019-03-01 00:14:42.588 300.0 Palm St at Willow St 37.3172979 -121.884995 312.0 San Jose Diridon Station 37.329732 ... 3803 Subscriber 1983 Female False 23 Thursday 2019-02-28 39 19.116667
7 1615 2019-02-28 23:41:06.766 2019-03-01 00:08:02.756 10.0 Washington St at Kearny St 37.79539293725452 -122.4047702550888 127.0 Valencia St at 21st St 37.7567083 ... 6329 Subscriber 1989 Male False 23 Thursday 2019-02-28 33 26.916667
8 1570 2019-02-28 23:41:48.790 2019-03-01 00:07:59.715 10.0 Washington St at Kearny St 37.79539293725452 -122.4047702550888 127.0 Valencia St at 21st St 37.7567083 ... 6548 Subscriber 1988 Other False 23 Thursday 2019-02-28 34 26.166667
9 1049 2019-02-28 23:49:47.699 2019-03-01 00:07:17.025 19.0 Post St at Kearny St 37.788975 -122.403452 121.0 Mission Playground 37.7592103 ... 6488 Subscriber 1992 Male False 23 Thursday 2019-02-28 30 17.483333
10 458 2019-02-28 23:57:57.211 2019-03-01 00:05:35.435 370.0 Jones St at Post St 37.7873267660624 -122.41327822208405 43.0 San Francisco Public Library (Grove St at Hyde... 37.7787677 ... 5318 Subscriber 1996 Female True 23 Thursday 2019-02-28 26 7.633333

10 rows × 21 columns

What is the structure of your dataset?¶

The dataset has 174952 rows and 21 columns features informations about goBike company rides during feburary month in 2019, information about ('duration_sec', 'start_time', 'end_time', 'start_station_id','start_station_name', 'start_station_latitude', 'start_station_longitude', 'end_station_id', 'end_station_name', 'end_station_latitude', 'end_station_longitude', 'bike_id', 'user_type', 'member_birth_year', 'member_gender', 'bike_share_for_all_trip', 'start_hour_of_the_day', 'start_day_of_the_week', 'start_date', 'member_age', 'duration_min'), columns are divided into 1 boolean, 2 category,2 datetime, 3 float,3 int, and 10 object

What is/are the main feature(s) of interest in your dataset?¶

I'm interested the most in finding a relation between Subscriber and Customers and how to convert customers into subscribes, by finding the common habits of Subscribers

What features in the dataset do you think will help support your investigation into your feature(s) of interest?¶

I think the finding a pattern between Subscriber habits and customers habits would help me

Univariate Exploration¶

In [17]:
def plt_custom_attr(title):
    plt.figure(figsize= [20, 10])
    plt.title(title, fontsize=24)
    plt.xticks(fontsize=16)
    plt.yticks(fontsize=16)

def plt_custom_attr_x_y(x_label, y_label):
    plt.xlabel(x_label, fontsize = 18)
    plt.ylabel(y_label, fontsize = 18)

I'll start by looking at the distribution of the main variables of interest

In [18]:
df.duration_min.max(), df.duration_min.min()
Out[18]:
(1409.1333333333334, 1.0166666666666666)
In [19]:
duration_min_adj = df.duration_min[df.duration_min.between(df.duration_min.quantile(0.01), df.duration_min.quantile(0.99))]
duration_min_adj.max()
Out[19]:
52.93333333333333
In [20]:
plt_custom_attr('Trips durations in minutes')
plt.hist(duration_min_adj, bins = 50)
plt_custom_attr_x_y('Durations in minutes','Count')
plt.xticks([0, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50]);

Most trip durations are between 0 and 10 minutes, trip duration increase at first till it reachs its peak at 4 mins then it start to decrease exponentially.

In [21]:
hours = ['00', '01', '02', '03' '04', '05', '06', '07', '08', '09', '10', '11', '12', '13', '14', '15', '16', '17', '18', '19', '20', '21', '22', '23']

plt_custom_attr('Trips start time of the day')
sns.countplot(data = df, x = 'start_hour_of_the_day', color = '#4169e1', order = hours)
plt_custom_attr_x_y('Hour of the day','Count');

There's two surges in start trip time, first one at 8 in the morning and second one is at 5 in the evening which alligns with the normal start and end working hours

In [22]:
weekday = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday']

plt_custom_attr('Trips per days of the week')
sns.countplot(data=df, x='start_day_of_the_week', color='#4169e1', order = weekday)
plt_custom_attr_x_y('Days of the week', 'Count');

Most busy days of the week are week days and there are drops in rides during weekend which could means that a lot of people using the bikes for going and coming from work

In [23]:
plt.figure(figsize= [20, 10])
plt_custom_attr('Trips per user type')
sns.countplot(data=df, x='user_type')
plt_custom_attr_x_y('User_type','Count');

<Figure size 1440x720 with 0 Axes>

The vast majority of rides made by users are by subscribers

In [24]:
plt_custom_attr('Trips per user gender')
sns.countplot(data = df, x = 'member_gender')
plt_custom_attr_x_y('Users gender','Count');

The majority of trip were made by male users 3 times more female useres

Discuss the distribution(s) of your variable(s) of interest. Were there any unusual points? Did you need to perform any transformations?¶

  • Most of the trips durations are between 1 and 10 minutes long which could mean that a lot of people are using bikes for short trips as mean of transportation.

  • Top start times for the trips are 8am and 5pm with average use in between those times and less rides at the night

  • Bike trips are more common during week days and much less during weekend

  • Subscribers are 8 times more than customers (but you can't tell number of users because there's no unique identfications for users)

  • Most of the trips were made by males than females

Of the features you investigated, were there any unusual distributions? Did you perform any operations on the data to tidy, adjust, or change the form of the data? If so, why did you do this?¶

  • Trip durations had some extreme long trips (24 hours) so I have cleaned the data and showed 99% percentile of the data with a new maximum trip of 52 minutes

Bivariate Exploration¶

In [25]:
plt_custom_attr('Trips duration over days of the week')
sns.barplot(data = df, x='start_day_of_the_week', y='duration_min', order = weekday, color='#4169e1')
plt_custom_attr_x_y('Days of the week','Average ride duration');

The average ride duration during week days is lower than average ride duration during weekend

In [26]:
plt_custom_attr('Average age among different users groups')
sns.violinplot(data = df, x = 'user_type', y = 'member_age', inner='quart')
plt_custom_attr_x_y('User type', 'Member age');
In [27]:
df.member_age.max()
Out[27]:
144
In [28]:
df = df[(np.abs(stats.zscore(df.member_age)) < 3)]
df.member_age.max()
Out[28]:
67
In [29]:
plt_custom_attr('Average age among different users groups')
sns.violinplot(data = df, x = 'user_type', y = 'member_age', inner='quart')
plt_custom_attr_x_y('User type', 'Member age');

The average age of customers and subscribers are 35 but at 1st quantile subscribers are slighter higher than customers

In [30]:
plt_custom_attr('Trips start time of the day Among different users types')
sns.countplot(data = df, x='start_hour_of_the_day', hue='user_type', order = hours)
plt_custom_attr_x_y('Hour of the day', 'Count');

For subsribers there's two surges in start trip time, first one at 8 in the morning and second one is at 5 in the evening, but with customers there's small difference between those 2 times

In [31]:
plt_custom_attr('Average ride duration among differen members age')
sns.regplot(data = df, x = 'member_age', y = 'duration_min', marker = '.' , x_jitter=0.5, scatter_kws={'alpha':3/10})
plt_custom_attr_x_y('Member age', 'Count');

There's no significant difference between ride time among different age groups

In [32]:
plt_custom_attr('Count of users among different genders')
sns.countplot(data = df, hue = 'user_type', x = 'member_gender')
plt_custom_attr_x_y('User gender', 'Count')
plt.legend(fontsize = 20);

The majority of trip were made by male users, but the ratio between customer to subscriber in female users are higher

In [33]:
plt_custom_attr('Start stations locations with most rides')
sns.countplot(data = df, y= 'start_station_name', order=df.start_station_name.value_counts().iloc[:15].index, hue = 'user_type')
plt_custom_attr_x_y('Count', 'Start station name')
plt.legend(fontsize = 20);

Talk about some of the relationships you observed in this part of the investigation. How did the feature(s) of interest vary with other features in the dataset?¶

  • The average age of customers and subscribers are 35 but at 1st quantile subscribers are slighter higher than customers
  • The average ride duration during week days is lower than average ride duration during weekend
  • For subsribers there's two surges in start trip time, first one at 8 in the morning and second one is at 5 in the evening, but with customers there's small difference between those 2 times
  • There's no significant difference between ride time among different age groups

Did you observe any interesting relationships between the other features (not the main feature(s) of interest)?¶

  • The relationship between number of rides and day of the week
  • The ralationship about the start time of the ride and ride duration

Multivariate Exploration¶

In [34]:
plt.figure(figsize= [20, 15])
fig = px.box(df, x="start_day_of_the_week", y="duration_min", color="bike_share_for_all_trip")

fig.update_layout(title = "Trips duration among different days of the week",
                  yaxis_title = "Ride duration",
                  xaxis_title = "Day of the week")
fig.show();

<Figure size 1440x1080 with 0 Axes>

There's more ride sharing among short duration rides

In [35]:
plt_custom_attr('Average ride duration among different user types during week days')
sns.pointplot(data = df, x='start_day_of_the_week', y='duration_min', hue='user_type', dodge=0.3, linestyles="", order = weekday)
plt_custom_attr_x_y('Days of the week', 'Average ride duration')
plt.legend(fontsize = 20);

Average ride duration are higher during weekend than weekdays amonth customers and subscribers

In [36]:
plt_custom_attr('Age distrubtion among different user gender and user type')
sns.violinplot(data=df, x="member_gender", y="member_age", hue="user_type", split=True, inner="quart", linewidth=1)
plt_custom_attr_x_y('User gender', 'User age')
plt.legend(fontsize = 20);
In [37]:
plt_custom_attr('Ride duration among member age and user type')
sns.stripplot(data = df, x = 'member_age', y = 'duration_min', hue = 'user_type', marker = '.' , jitter = 0.5 , alpha = 0.3)
plt_custom_attr_x_y('Member age', 'Ride duration')
plt.legend(fontsize = 20);

Talk about some of the relationships you observed in this part of the investigation. Were there features that strengthened each other in terms of looking at your feature(s) of interest?¶

  • There's more ride sharing among short duration rides
  • Average ride duration are higher during weekend than weekdays amonth customers and subscribers

Were there any interesting or surprising interactions between features?¶

  • Although there are more rides during weekdays but the average of ride duration during weekend is higher